Udemy – Production AI Agents with JavaScript: LangChain & LangGraph

Most LangChain and LangGraph courses are Python-first. This one is built from the ground up for JavaScript & TypeScript engineers who want real, shippable agentic systems—not disconnected demos. You’ll build a sequence of end-to-end projects that mirror how modern teams ship AI features: clean TypeScript code, clear APIs, JSON contracts, LangGraph orchestration, RAG, proper vector stores, and real Next.js frontends wired to real agents.

By the end, you’ll know exactly how to go from idea → design → implementation → observability → deployment in the JS ecosystem.

Here’s what we’ll cover in Phase 1:

• Intro & Mindset
  • How this course works, what it is / isn’t, and how to follow.
  • Choosing models (OpenAI / Gemini / Groq / local) smartly for cost, speed & reliability.
  • How all projects connect into a reusable “agent platform” you can extend.
  • Modern AI app architecture: UI → orchestration → models → tools → storage.
  • Simple, honest definition of AI agents and real-world use cases.
  • Chains vs agents: when a chain is enough, when an agent is worth it.
  • Where LangChain.js fits, where LangGraph.js fits, and how they work together.
  • JSON-first mindset teaser: why strings lie and schemas save you.
  • TS/Node project setup, tsconfig, env patterns, scripts.
  • Multi-provider setup: OpenAI, Gemini, Groq via a single provider factory.
  • First “Hello Agent” function that runs like a clean backend primitive, not a toy script.
  • Tokens, context windows, cost-aware thinking.
  • Sampling knobs: temperature, top_p, max_tokens in practical terms.
  • Chat vs tools; why structured outputs beat ad-hoc prompts.
  • Zod schemas as contracts for every response.
  • Validate → repair → fallback strategies to keep agents stable.